In a wireless communication system, Radio Resource Allocation (RRA) algorithms are responsible for the management of the scarce resources available in the radio interface between a transmitter (such as a radio base station) and a receiver (such as a cellphone or other wireless terminal). In a multi-user and/or multi-service scenario, where one transmitter is transmitting user-specific data to each of several users and/or for each of several data services, these scarce resources, which include transmitter power, time slots, and frequency “chunks,” must be divided among the users and/or services.
In multi-service scenarios, RRA algorithms can be used to satisfy Quality of Service (QoS) requirements for the connected flows, where a “flow” is the data (e.g., IP packets) corresponding to a particular user service, such as VoIP, web browsing, email, etc. (Other terms are “service data flow” and “data flow”—these terms may be considered to be interchangeable for the purposes of this document.) In general, the flows in a multi-service scenario have heterogeneous demands and different channel quality states.
However, RRA solutions that maximize spectral efficiency are not generally capable of fulfilling the QoS demands of the connected flows. (See E. A. Jorswieck, A. Sezgin, and X. Zhang, “Framework for Analysis of Opportunistic Schedulers: Average Sum Rate vs. Average Fairness,” in Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops, 2008. WiOPT 2008. 6th International Symposium on, Berlin, April 2008, pp. 100-105.) In the context of the problem of unconstrained data rate maximization in the context of Orthogonal Frequency Division Multiple Access (OFDMA) networks with single-antenna transceivers or Single-Input Single-Output (SISO), it has been shown that the optimum solution to the problem of frequency chunk allocation is obtained by a low-complexity algorithm with a simple idea: assign the resources to the flows with best channel quality on them. (J. Jang and K. B. Lee, “Transmit Power Adaptation for Multiuser OFDM Systems,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 2, pp. 171-178, February 2003.) Further research has been directed to the problem of frequency resource allocation for maximization of the total downlink data rate in the SISO scenario, subject to the constraint that all flows should have minimum data rate requirements fulfilled. Because the formulated problem is difficult to solve optimally, suboptimum solutions have been proposed. (Y. J. Zhang and K. B. Letaief, “Multiuser Adaptive Subcarrier-and-Bit Allocation with Adaptive Cell Selection for OFDM Systems,” IEEE Transactions on Wireless Communications, vol. 3, no. 5, pp. 1566-1575, September 2004; P. Tejera, W. Utschick, G. Bauch, and J. A. Nossek, “Subchannel Allocation in Multiuser Multiple-Input Multiple-Output Systems,” Information Theory, IEEE Transactions on, vol. 52, no. 10, pp. 4721-4733, October 2006.)
Making use of advanced signal processing algorithms, Multiple-Input Multiple-Output (MIMO) techniques represent a key technology to meet the requirements established by International Mobile Telecommunications (IMT)-Advanced, and feature prominently in the transmission techniques standardized by the 3rd-Generation Partnership Project (3GPP) for use in the so-called Long-Term Evolution (LTE) networks, which utilize OFDMA technology. However, MIMO technology presents new challenges for RRA, which has to cope with the introduction of the spatial dimension.
The use of multiple antennas at the transmitter and at the receiver allows for the simultaneous use of the same time-frequency resource by different flows, through the allocation of separate so-called Spatial Subchannels to the different flows. In the discussion that follows, the term “Space-Division Multiple Access (SDMA) group” refers to the set of flows that share a specific frequency resource.
The spatial dimension provided by MIMO systems can be exploited by RRA in order to improve the provided QoS for the flows. However, due to the additional complexity, the solution of the problem of unconstrained data rate maximization in the MIMO scenario employs computationally expensive, non-linear techniques that are not feasible for practical scenarios. (P. Tejera, W. Utschick, G. Bauch, and J. A. Nossek, “Subchannel Allocation in Multiuser Multiple-Input Multiple-Output Systems,” Information Theory, IEEE Transactions on, vol. 52, no. 10, pp. 4721-4733, October 2006.)
Previous solutions to unconstrained data rate maximization in OFDMA networks do not take into account QoS issues that are of utmost concern in modern wireless networks. Other studies that do consider QoS guarantees have an important limitation. In particular, the resource assignment problem in these studies is based on an assumption that all flows are from the same service class and a requirement that all flows should have the QoS constraints fulfilled. RRA solutions applicable to a more flexible scenario where only a fraction of the multiservice flows should have the QoS requirements fulfilled are desired. These solutions should also take into account the introduction of the spatial dimension enabled by MIMO techniques. Finally, the provided solutions should be simple, in order to allow their use in practical scenarios.